The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit a...The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.展开更多
Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving...Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving of taxi drivers under three different trip categories.Trip origin is considered a transition from without ride-order to with ride-order travelling or from with ride-order to occupied travelling,and a destination as a transition from occupied to without ride-order travelling and vice versa.Distracted driving is characterized by driver interference,driver mobile use and some entertainment aspects,while specific harmful and risky actions are considered for aggressive driving.High-resolution and real-time kinematic parameters of taxis were recorded by the in-vehicle recorder VBOX for overall 562 trips.The distracted driving parameters and aggressive driving actions were monitored through python data collector web application that was specially programmed for this particular research.Besides dual dash cam(i.e.,front and inside car camera),drivers’ whole day driving history from their Chinese ride-hailing Di Di smart application was used to differentiate occupied trips,unoccupied trips with ride-order and unoccupied trips without ride-order.Structural equation modeling(SEM) is practiced in this paper to understand the influence of distracted driving indicators on aggressive driving behaviors.The multi-group model analysis of SEM indicated that handling distracted risky driving could control aggressive driving behavior up to 96% and 98% inunoccupied without ride-order trips and unoccupied trips with ride-order respectively.The model has also identified the sensitive risky driving indicators for each group separately.展开更多
基金the Humanities and Social Science Foundation of the Ministry of Education of China(Grant No.20YJCZH121).
文摘The urban transit fare structure and level can largely affect passengers’travel behavior and route choices.The commonly used transit fare policies in the present transit network would lead to the unbalanced transit assignment and improper transit resources distribution.In order to distribute transit passenger flow evenly and efficiently,this paper introduces a new distance-based fare pattern with Euclidean distance.A bi-level programming model is developed for determining the optimal distance-based fare pattern,with the path-based stochastic transit assignment(STA)problem with elastic demand being proposed at the lower level.The upper-level intends to address a principal-agent game between transport authorities and transit enterprises pursing maximization of social welfare and financial interest,respectively.A genetic algorithm(GA)is implemented to solve the bi-level model,which is verified by a numerical example to illustrate that the proposed nonlinear distance-based fare pattern presents a better financial performance and distribution effect than other fare structures.
基金supported by the National Key R&D Program of China(2018YFB1601600)。
文摘Risky driving behavior of taxi drivers typically evaluated for full operation or sometimes sorted into occupied and empty running trips.In this paper,we simultaneously analyze aggressive driving and distracted driving of taxi drivers under three different trip categories.Trip origin is considered a transition from without ride-order to with ride-order travelling or from with ride-order to occupied travelling,and a destination as a transition from occupied to without ride-order travelling and vice versa.Distracted driving is characterized by driver interference,driver mobile use and some entertainment aspects,while specific harmful and risky actions are considered for aggressive driving.High-resolution and real-time kinematic parameters of taxis were recorded by the in-vehicle recorder VBOX for overall 562 trips.The distracted driving parameters and aggressive driving actions were monitored through python data collector web application that was specially programmed for this particular research.Besides dual dash cam(i.e.,front and inside car camera),drivers’ whole day driving history from their Chinese ride-hailing Di Di smart application was used to differentiate occupied trips,unoccupied trips with ride-order and unoccupied trips without ride-order.Structural equation modeling(SEM) is practiced in this paper to understand the influence of distracted driving indicators on aggressive driving behaviors.The multi-group model analysis of SEM indicated that handling distracted risky driving could control aggressive driving behavior up to 96% and 98% inunoccupied without ride-order trips and unoccupied trips with ride-order respectively.The model has also identified the sensitive risky driving indicators for each group separately.
